Non-small Cell Lung Cancer Patients Research Articles

Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer.

This study investigates the association between baseline CT body composition parameters and clinical outcomes in patients with resectable non-small cell lung cancer (NSCLC). Patients who underwent surgical resection for NSCLC between January 2006 and December 2017 were retrospectively enrolled in this multicenter study. Body composition metrics, including the area of skeletal muscle, intermuscular adipose tissue, subcutaneous adipose tissue, visceral adipose tissue, muscle radiodensity, and derivative parameters from five basic metrics mentioned before, were calculated based on preoperative non-contrast-enhanced chest CT images at L1 level. The Cox proportional hazards regression analysis was used to evaluate the association between body composition metrics and survival outcomes including overall survival (OS) and disease-free survival (DFS). A total of 2712 patients (mean age, 61.53 years; 1146 females) were evaluated. A total of 635 patients (23.41%) died. 465 patients (19.51%) experienced recurrence and/or distant metastasis. After multivariable adjustment, skeletal muscle index (SMI, HR = 0.86), intermuscular adipose index (IMAI, HR = 1.49), and subcutaneous adipose index (SAI, HR = 0.96) were associated with OS. Similar results were found after stratification by gender, TNM stage, and center. There was no significant association between all body composition metrics and DFS (all p > 0.05). The body composition metrics significantly enhance the model including clinicopathological factors, resulting in an improved AUC for predicting 1-year and 3-year OS, with AUC values of 0.707 and 0.733, respectively. SMI, IMAI, and SAI body composition metrics have been identified as independent prognostic factors and may indicate mortality risk for resectable NSCLC patients. Our findings emphasize the significance of muscle mass, quality, and fat energy storage in clinical decision-making for patients with non-small cell lung cancer (NSCLC). Nutritional and exercise interventions targeting muscle quality and energy storage could be considered for patients with NSCLC. Multiparameter body composition analysis is associated with the clinical outcome in NSCLC patients. Assessing muscle mass, quality, and adipose tissue helps predict overall survival in NSCLC. The quantity and distribution of body composition can contribute to unraveling the adiposity paradox.

Clinical efficacy and safety of sintilimab plus oral vinorelbine as first-line treatment for newly diagnosed stage IIIB–IV nonsmall cell lung cancer patients with performance status 2 or age ≥75 years

This study aimed to evaluate the efficacy and safety of sintilimab combined with oral vinorelbine in newly diagnosed patients with stage IIIb to IV nonsmall cell lung cancer (NSCLC) who had an Eastern Cooperative Oncology Group performance status (PS) of 2 or over 75 years of age during the initial treatment. This prospective single-center single-arm study enrolled patients with histologically confirmed NSCLC. Eligible patients were administered sintilimab and vinorelbine. The primary endpoint was progression-free survival (PFS), and the secondary endpoints included objective response rate (ORR) and disease control rate (DCR). Furthermore, this study assessed indicators of treatment response and safety. From September 2020 to December 2023, 60 eligible patients were enrolled in the Respiratory Department of Shanxi Cancer Hospital. Following treatment, PFS was 9.1 months, and ORR and DCR were 39.6 and 63.79%, respectively. In addition, there was a reduction in blood tumor marker levels and enhanced immune function. Adverse reactions had a relatively low incidence and primarily consisted of grade 1–2 cases. Sintilimab plus oral vinorelbine showed promising efficacy and safety as a first-line treatment strategy for patients with NSCLC with PS 2 or elderly patients. It also optimizes immune function in patients with NSCLC.

Epsin3 promotes non-small cell lung cancer progression via modulating EGFR stability

BackgroundThe abnormal expression and overactivation of the epidermal growth factor receptor (EGFR), a typical cancer marker for non-small cell lung cancer (NSCLC), are closely related to the tumorigenesis and progression of NSCLC. However, the endocytosis mechanism of EGFR in lung cancer is not yet known. Epsin3 (EPN3), a member of the endocytic adaptor protein family, is essential for the endocytosis of multiple receptors. In this study, we aimed to investigate the role of EPN3 in modulating EGFR function, its effects on NSCLC progression, and its potential involvement in tyrosine kinase inhibitor (TKI) resistance, which remains a significant hurdle in NSCLC treatment.ResultsOur findings revealed that the expression of EPN3 is significantly up-regulated in NSCLC patients. Elevated EPN3 expression was proportional to shorter overall survival in patients with NSCLC. Functional analyses revealed that EPN3 directly interacts with EGFR, enhancing its recycling to the plasma membrane and preventing its degradation via the lysosomal pathway. This stabilization of EGFR led to sustained downstream signalling, promoting NSCLC cell proliferation and migration. Notably, mutations in the EGFR tyrosine kinase domain, which typically confer resistance to TKIs, did not alter the regulatory effect of EPN3.ConclusionsEPN3 enhances EGFR signalling by promoting its recycling and stability, contributing to NSCLC progression and TKI resistance. Targeting EPN3 could offer a novel therapeutic strategy to overcome drug resistance in EGFR-driven NSCLC.

Study on postoperative survival prediction model for non-small cell lung cancer: application of radiomics technology workflow based on multi-organ imaging features and various machine learning algorithms

ObjectiveThis study aims to construct an effective prediction model for the two-year postoperative survival probability of patients with non-small cell lung cancer (NSCLC). It particularly focuses on integrating radiomics features, including the erector spinae and whole-lung imaging features, to enhance the accuracy and stability of prognostic predictions.Materials and methodsThe study included 37 NSCLC patients diagnosed and surgically treated at the First Affiliated Hospital of Anhui Medical University from January 2020 to December 2021. The average age of the patients was 59 years, with the majority being female and non-smokers. Additionally, CT imaging data from 98 patients were obtained from The Cancer Imaging Archive (TCIA) public database. All imaging data were derived from preoperative chest CT scans and standardized using 3D Slicer software. The study extracted radiomic features from the tumor, whole lung, and erector spinae muscles of the patients and applied 11 machine learning algorithms to construct prediction models. Subsequently, the classification performance of all constructed models was compared to select the optimal prediction model.ResultsUnivariate Cox regression analysis showed no significant correlation between the collected clinical factors and patient survival time. In the external validation set, the K-Nearest Neighbors (KNN) model based on bilateral erector spinae features performed the best, with accuracy and AUC (Area Under the Curve) values consistently above 0.7 in both the training and external testing sets. Among the prognostic models based on whole-lung imaging features, the AdaBoost model also performed well, but its AUC value was below 0.6 in the external validation set, indicating overall classification performance still inferior to the KNN model based on erector spinae features.ConclusionThis study is the first to introduce erector spinae imaging features into lung cancer research, successfully developing a stable and well-performing prediction model for the postoperative survival of NSCLC patients. The research results provide new perspectives and directions for the application of radiomics in cancer research and emphasize the importance of incorporating multi-organ imaging features to improve the accuracy and stability of prediction models.

Non-cancerous CT findings as predictors of survival outcome in advanced non-small cell lung cancer patients treated with first-generation EGFR-TKIs.

To identify non-cancerous factors from baseline CT chest affecting survival in advanced non-small cell lung cancer (NSCLC) treated with first-generation Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors (EGFR-TKIs). Retrospective study of 172 advanced NSCLC patients treated with first-generation EGFR-TKIs as a first-line systemic treatment (January 2012 to September 2022). Baseline CT chest assessed visceral/subcutaneous fat (L1 level), sarcopenia, and myosteatosis (multiple levels), main pulmonary artery (MPA) size, MPA to aorta ratio, emphysema, and bone mineral density. Cox regression analyzed prognostic factors at 18-month outcome. Median overall survival was 17.57 months (14.87-20.10) with 76 (44.19%) patients died at 18 months. Deceased had lower baseline BMI (21.10 ± 3.44) vs. survived (23.25 ± 4.45) (p < 0.001). Univariable analysis showed 5 significant prognostic factors: low total adiposity with/without cutoff [HR 2.65 (1.68-4.18), p < 0.001; 1.00 (0.99-1.00), p = 0.006;], low subcutaneous adipose tissue (SAT) with/without cutoff [HR 1.95 (1.23-3.11), p = 0.005; 0.99 (0.98-0.99), p = 0.005], low SAT index (SATI) with/without cutoff [1.74 (1.10-2.78), p = 0.019; 0.98 (0.97-0.99), p = 0.003], high VSR [1.67 (1.06-2.62), p = 0.026], and high MPA size with/without cutoff [2.23 (1.23-4.04), p = 0.005; 1.09 (1.04-1.16), p = 0.001]. MPA size, MPA size > 29 mm, and total adiposity ≤85 cm2 remained significant in multivariable analysis, adjusted by BMI [HR 1.14 (1.07-1.21), p < 0.001; 3.10 (1.81-5.28), p < 0.001; 3.91 (1.63-9.40), p = 0.002]. There was no significant difference of sarcopenic and myosteatotic parameters between the two groups. In advanced EGFR-mutated NSCLC patients, assessing pre-treatment prognosis is warranted to predict the survival outcome and guide decision regarding EGFR-TKI therapy. Enlarged MPA size, low total adiposity, and low subcutaneous fat (lower SAT, lower SATI, and higher VSR) are indicators of poor survival. Large MPA size (>29 mm) or low total adiposity (≤85 cm2) alone predict 18-month death.

Feasibility of detecting non-small cell lung cancer using exhaled breath condensate metabolomics

Lung cancer is one of the most common malignancy in the world, and early detection of lung cancer remains a challenge. The exhaled breath condensate (EBC) from lung and trachea can be collected totally noninvasively. In this study, our aim is to identify differential metabolites between non-small cell lung cancer (NSCLC) and control EBC samples and discriminate NSCLC group from control group by orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models. The EBC differential metabolites between NSCLC patients (n= 29) and controls (n= 24) (20 healthy and 4 benign individuals) were identified using ultra-performance liquid chromatography-high resolution mass spectrometry based untargeted metabolomics method. The upregulated metabolites in EBC of NSCLC included amino acids and derivatives (phenylalanine, tryptophan, 1-carboxyethylisoleucine/1-carboxyethylleucine, and 2-octenoylglycine), dipeptides (leucyl-phenylalanine, leucyl-leucine, leucyl-histidine/isoleucyl-histidine, and prolyl-valine), and fatty acids (tridecenoic acid, hexadecadienoic acid, tetradecadienoic acid, 9,12,13-trihydroxyoctadec-10-enoic acid/9,10,13-trihydroxyoctadec-11-enoic acid (9,12,13-TriHOME/9,10,13-TriHOME), 3-hydroxysebacic acid/2-hydroxydecanedioic acid, 9-oxooctadeca-10,12-dienoic acid/9,10-Epoxy-12,15-octadecadienoate (9-oxoODE/9(10)-EpODE), and suberic acid). The downregulated metabolites in EBC of NSCLC were 3,4-methylenesebacic acid, 2-isopropylmalic acid/3-isopropylmalic acid/2,3-dimethyl-3-hydroxyglutaric acid, and trimethylamine-N-oxide. The OPLS-DA model based on 5 EBC metabolites achieved 86.2% sensitivity, 83.3% specificity and 84.9% accuracy, showing a potential to distinguish NSCLC patients from controls.

Efficacy and safety of aumolertinib in EGFR-mutated non-small cell lung cancer with leptomeningeal metastasis: a single‑center retrospective study.

To evaluate the efficacy and safety of aumolertinib in treating non-small cell lung cancer (NSCLC) patients with leptomeningeal metastasis (LM) and epidermal growth factor receptor (EGFR) mutations. We conducted a retrospective analysis of clinical data from 79 patients with EGFR-mutated advanced NSCLC treated with aumolertinib after being diagnosed with LM at Shandong Cancer Hospital and Institute between April 2020 and July 2023. We evaluated overall survival (OS), progression-free survival (PFS), LM-PFS, and safety. Patient prognosis was assessed using Kaplan-Meier and Cox regression analyses. The median follow-up duration was 19.8 months (95% CI: 16.2-23.4), and 16 (20.3%) patients had previously used third-generation EGFR-TKI. The median LM-PFS was 10.6 months (95% CI: 8.6-12.5). The overall response rate (ORR) for LM was 53.2%, while the disease control rate (DCR) reached 91.1%. Among the 67 (84.8%) patients presenting with symptoms attributable to LM, 60 reported improved or stable symptoms. The median OS was 17.7 months (95% CI: 13.7-21.7), while the median PFS was 9.7 months (95% CI: 7.5-11.9). The systemic ORR and DCR were 38.0% and 87.3%, respectively. Multivariate Cox regression analysis identified L858R mutations (hazard ratio [HR] = 2.22, P = 0.030), prior systemic therapy (HR = 3.89, P < 0.001), ECOG PS ≥ 2 (HR = 4.06, P < 0.001) and ≥ 3 extracranial organ metastases (HR = 2.20, P = 0.025) as independent negative predictors of OS. Creatine kinase elevation (HR = 0.41, P = 0.018) was an independent predictor of better OS. Treatment-related adverse events occurred in 61 patients (77.2%), predominantly as grade 1 or 2. Aumolertinib showed potential in treating EGFR-mutated NSCLC patients with LM, with a tolerable safety profile.

Targeting HVEM-GPT2 axis: a novel approach to T cell activation and metabolic reprogramming in non-small cell lung cancer therapy.

The modulation of tumor microenvironments through immune checkpoint pathways is pivotal for the development of effective cancer immunotherapies. This study aims to explore the role of HVEM in non-small cell lung cancer (NSCLC) microenvironment. The lung cancer datasets for this study were directly downloaded from The Cancer Genome Atlas (TCGA). Single-cell data were sourced from the Tumor Immune Single-cell Hub (TISCH). Multiplex immunohistochemistry (mIHC) was used to explore the cellular composition and spatial distribution of HVEM in lung cancer immune microenvironment. Theimmunemicroenvironmentof HVEM KO mice bearing mouse lung cancer cell was also evaluated. Co-cultured system and phenotype assays facilitated the examination of Jurkat T cells' effect on A549 and H1299 lung cancer cells. Quantitative PCR and Western blotting determined gene and protein expression, respectively, cellular respiration was measured through oxygen consumption rate (OCR) assays. Lung cancer cells co-cultured with Jurkat T cells were xenografted into nude mice to evaluate tumor growth and metastatic potential. Next, RNA-seq, COIP, Dual-luciferasereporter experiment, and CHIP-seq were used to explore the potential underlying mechanism. In our study, we investigated the role of HVEM in the microenvironment of NSCLC and its implications in immunotherapy. Crucially, HVEM, part of the tumor necrosis factor receptor superfamily, influences T cell activation, potentially impacting immunotherapeutic outcomes. Using the TIDE algorithm, our results showcased a link between HVEM levels and immune dysfunction in NSCLC patients. Delving deeper into the NSCLC microenvironment, we found HVEM predominantly expressed in T cell subpopulations. CD8 + HVEM + and CD4 + HVEM + indicated better prognosis in lung adenocarcinoma tissue microarray using multiplex immunohistochemistry. Activated T cells, particularly from the Jurkat cell line, significantly inhibited NSCLC progression, reducing both proliferation and invasion capabilities of A549 and H1299 lung cancer cell lines. In vivo models reinforced these observations. Manipulating HVEM expression revealed its essential role in T cell survival and activation. In addition, animal experiments revealed the importance of HVEM in maintaining activated peripheral immunity and inflamed local tumor microenvironment. Furthermore, our data suggest that HVEM is pivotal in T cell metabolic reprogramming, transitioning from oxidative phosphorylation to aerobic glycolysis. RNA sequencing illuminated a potential relationship between HVEM and GPT2, an enzyme tied to amino acid metabolism and cellular energetics. Subsequent experiments confirmed that HVEM's influence on T cell activation and metabolism is potentially mediated through its regulation of GPT2. In addition, GATA1 was validated to regulate HVEM expression in activated Jurkat T cells. Our study establishes that HVEM significantly influences T cell functionality and NSCLC cell dynamics, pinpointing the HVEM-GPT2 axis as a promising target for NSCLC therapy.

Overall Staging Prediction for Non-Small Cell Lung Cancer (NSCLC): A Local Pilot Study with Artificial Neural Network Approach

Background: Non-small cell lung cancer (NSCLC) has been the most common cancer globally in the recent decade. CT is the most common imaging modality for the initial diagnosis of NSCLC. The gold standard for definitive diagnosis is the histological evaluation of a biopsy or surgical sample, which usually requires a long processing time for the confirmation of diagnosis. This study aims to develop artificial intelligence models to predict overall staging based on patient demographics and radiomics retrieved from the initial CT images, so as to prioritize later-stage patients for histology evaluation to facilitate cancer diagnosis. Method: Two cohorts of NSCLC patient datasets were utilized for this study. The NSCLC-radiomics dataset from The Cancer Imaging Archive (TCIA) was divided into 70% for the training group and 30% for the internal testing group. Another cohort from a local hospital was collected for the an external testing group. Patient demographics and 107 radiomic features were retrieved from the gross tumor volume delineated by clinical oncologists on CT images. Artificial neural networks were used to build models for NSCLC overall staging (stage I, II, or III) prediction. Four traditional classifiers were also adopted to build models for comparison. Result: The proposed feed-forward neural network (FFNN) model showed good performance in predicting overall staging with an accuracy of 88.84%, 76.67%, and 74.52% in overall accuracies in validation, internal cohort testing, and external cohort testing, respectively. The sensitivity and specificity are balanced in all the stages, with average precision and F1 score in each of the stages. Conclusion: The FFNN demonstrated good performance in overall staging prediction for NSCLC patients. It has the benefit of predicting multiple overall stages in a single model. The software required and the proposed model are simple. It can be operated on a general-purpose computer in the radiology department. The application will eventually be used as a prediction tool to prioritize the biopsy or surgery sample for histological analysis and molecular investigation, thus shortening the time for diagnosis by pathologists, which supports the triage of patients for further testing.

A comparative study of surgery versus radiation therapy on the risk of cardiovascular disease mortality in patients with early-stage non-small cell lung cancer

Objective: Non-small cell lung cancer (NSCLC) lung cancer continues to be a substantial issue in public health, and cardiovascular disease (CVD) is also an important cause of death in NSCLC patients. There is a lack of studies comparing the effects of surgery and radiation therapy on the risk of CVD-mortality in patients with early-stage NSCLC. This study planned to compare the effects of surgery alone and radiation therapy alone on the risk of CVD-mortality in patients with early-stage NSCLC. Methods: In this cohort study, the data of 32896 participants with NSCLC at stage I or stage II in 2010-2015 were retrieved from the surveillance, epidemiology, and end results (SEER) database. The primary endpoint of this study was CVD mortality, indicating patients died of CVDs and the follow-up was ended in 2020. Univariable Cox regression model was applied to identify covariates. The associations of surgery or radiation therapy with CVD-mortality in in patients with early-stage NSCLC were evaluated via univariable and multivariable Cox regression models and Fine-Gray competitive risk model. Hazards ratio (HR) and confidence interval (CI) were computed. Results: The median follow-up time was 48.00 (17.00, 60.00) months. There were 854 (6.45%) participants died of CVD in the radiation therapy group and 729 (5.35%) participants died of CVD in the surgery group. After adjusting for confounding factors, the elevated risk of CVD mortality in patients with early-stage NSCLC was observed in patients receiving radiation therapy compared to those receiving surgery (HR=2.33, 95%CI: 2.02-2.69). In the competing risk model, the risk of CVD mortality in patients with early-stage NSCLC was also increased in patients receiving radiation therapy (HR=1.37, 95%CI: 1.2.6-1.55). In the PSM group, the risk of CVD mortality in patients with early-stage NSCLC was also increased in patients who underwent radiation therapy (HR=2.62, 95%CI: 2.12-3.24). Subgroup analysis also revealed that radiation therapy was correlated with increased risk of CVD mortality in NSCLC patients with tumor size ≥50 mm or &lt;50 mm, the original primary site in the left or right, histologic types of squamous cell NSCLC or adenocarcinoma NSCLC, stage I and II, and patients ≥ 65 years or &lt; 65 years. Conclusions: Radiation therapy was associated with elevated risk of CVD mortality compared to surgery in patients with early-stage NSCLC.

Artificial intelligence for diagnosis and predictive biomarkers in Non-Small cell lung cancer Patients: New promises but also new hurdles for the pathologist.

The rapid development of artificial intelligence (AI) based tools in pathology laboratories has brought forward unlimited opportunities for pathologists. Promising AI applications used for accomplishing diagnostic, prognostic and predictive tasks are being developed at a high pace. This is notably true in thoracic oncology, given the significant and rapid therapeutic progress made recently for lung cancer patients. Advances have been based on drugs targeting molecular alterations, immunotherapies, and, more recently antibody-drug conjugates which are soon to be introduced. For over a decade, many proof-of-concept studies have explored the use of AI algorithms in thoracic oncology to improve lung cancer patient care. However, despite the enthusiasm in this domain, the set-up and use of AI algorithms in daily practice of thoracic pathologists has not been operative until now, due to several constraints. The purpose of this review is to describe the potential but also the current barriers of AI applications in routine thoracic pathology for non-small cell lung cancer patient care and to suggest practical solutions for rapid future implementation.

Stage IIIA Non-Small Cell Lung Cancer Treatment and Outcomes: A Single Institution Retrospective Analysis.

Stage III non-small cell lung cancer (NSCLC) treatment remains challenging, with a multitude of treatment options available. We assessed Stage IIIA NSCLC outcomes by treatment received. We performed a single-institution retrospective review of NSCLC patients with Stage IIIA disease treated January 01, 2010-March 01, 2022. Demographics, treatments, outcomes, and failure patterns were collected. Progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan-Meier analysis. Failure patterns were assessed for differences using chi-square analysis. Of 270 Stage III NSCLC patients, 134 had Stage IIIA disease with a median follow-up of 29.9 months and a median age of 66 years (IQR 60-75). 66 (49.3%) patients were male, and 105 (78.4%) were current/former smokers (with 30 median pack-years). Patients were treated with definitive radiation with/without chemotherapy (CRT; n = 77, 57.5%), surgery with neoadjuvant chemotherapy and/or radiation (Neoadj; n = 42, 31.3%), and surgery without neoadjuvant therapy (Surg; n = 15, 11.2%). Median PFS was 25.4 months (95% CI 12.5-42.6) for CRT, 22.6 months (95% CI 12.2-44.4) for Neoadj, and 22.8 months (95% CI 5.2-NA) for Surg with no significant intergroup difference (p = 0.99). Median OS was 57.0 months (95% CI 37.4-77.5) for CRT, 51.5 months (95% CI 36.7-65.5) for Neoadj, and 35.3 months (95% CI 16.8-NR) for Surg with no significant intergroup difference (p = 0.99). In this single institution retrospective study, we find no significant differences in PFS, OS, and failure patterns between patients with Stage IIIA NSCLC treated with definitive (chemo)radiation and surgery with or without neoadjuvant therapy. Further work in the immunotherapy era is needed.

A facile liquid biopsy assay for highly efficient CTCs capture and reagent-less monitoring of immune checkpoint PD-L1 expression on CTCs with non-small cell lung cancer patients

A facile liquid biopsy assay for highly efficient CTCs capture and reagent-less monitoring of immune checkpoint PD-L1 expression on CTCs with non-small cell lung cancer patients

Very first real-world data on zongertinib use in non-small cell lung cancer patients with HER2 mutations: A brief report

Very first real-world data on zongertinib use in non-small cell lung cancer patients with HER2 mutations: A brief report

Does metformin use improve the survival in patients with early-stage surgically treated non-small cell lung cancer patients?

Does metformin use improve the survival in patients with early-stage surgically treated non-small cell lung cancer patients?

Efficacy of PD-1 blockade plus chemotherapy in patients with oncogenic-driven non-small-cell lung cancer

BackgroundPD-1 blockade plus chemotherapy has become the first-line standard of care for patients with advanced non-small-cell lung cancer (NSCLC) without oncogenic drivers. Oncogenic-driven advanced NSCLC showed limited response to PD-1 blockade monotherapy or chemotherapy alone. Whether NSCLC patients with oncogenic drivers could benefit from PD-1 blockade plus chemotherapy remains undetermined.MethodsThree hundred twelve NSCLC patients with at least one oncogenic driver alteration received PD-1 plus chemotherapy or each monotherapy were retrospectively identified. Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were compared to evaluate the therapeutic outcomes differences among patients with different oncogenic drivers.ResultsOne hundred sixty-two patients received PD-1 blockade plus chemotherapy, 57 received PD-1 blockade monotherapy and 93 received chemotherapy alone were included. Oncogenic driver mutations including KRAS (31.4%), EGFR (28.8%), HER2 (14.7%), BRAF (10.6%), RET (7.4%), and other mutations (7.1%) were identified. Patients with oncogenic drivers who received PD-1 blockade plus chemotherapy had significantly better outcomes compared to those received PD-1 blockade or chemotherapy alone (ORR: 51% vs. 18% vs. 25%, P < 0.001; median PFS: 10.0 [95% CI: 8.9–12.6] vs. 3.7 [95% CI: 2.9–5.1] vs. 5.3 [95% CI: 4.5–6.2] months, P < 0.001; median OS: 26.0 [95% CI: 23.0–30.0] vs. 14.3 [95% CI: 9.6–19.8] vs. 16.1 [95% CI: 11.6–21.9] months, P < 0.001). The superior efficacy was consistently found in separate analyses for patients received first-line and second/third line treatments. Among individual gene alterations, patients with KRAS, EGFR, or BRAF mutations treated with PD-1 blockade plus chemotherapy achieved markedly improved PFS and OS than those received PD-1 blockade or chemotherapy alone. Multivariate Cox regression analysis revealed that PD-1 blockade plus chemotherapy was independently associated with better PFS and OS.ConclusionPD-1 blockade plus chemotherapy demonstrated superior efficacy than PD-1 blockade monotherapy or chemotherapy alone in patients with oncogenic-driven advanced NSCLC, particularly in KRAS, EGFR and BRAF subgroups. These findings suggest that PD-1 blockade plus chemotherapy may be considered as an optional treatment option for patients without available targeted therapies.

The 5-factor modified frailty index as a prognostic factor following stereotactic radiosurgery for metastatic disease to the brain from non-small cell lung cancer: A multi-center cohort analysis.

Non-small cell lung cancer (NSCLC) patients often develop brain metastases (BMs), complicating management. We have shown that increasing frailty is associated with decreased overall survival (OS) and central nervous system progression free survival (PFS) for patients undergoing stereotactic radiosurgery (SRS) to BMs. Leveraging the International Radiosurgery Research Foundation, we sought to expand upon these findings, in NSCLC specifically. Across four institutions, 193 patients with (≥1) NSCLC derived BMs with minimum 3months of clinical/radiographic follow-up were analyzed. Primary outcomes included OS and PFS. Patients were stratified utilizing the mFI-5 into pre-frail (0-1), frail (2), and severely frail (3+). Increased frailty was associated with diminished OS (frail hazard ratio (HR)=2.49, 95% CI [1.61-3.85]; severely frail HR = 2.65, 95% CI [1.57-4.45]). The 6-month post-SRS survival rate was 86.1%, 69.5% and 54.5% for pre-frail, frail and severely frail patients, respectively (p<0.001). Frailty was not significantly predictive of time to PFS on multivariate Cox Proportional Hazards analysis although there was a trend towards diminished PFS with increasing frailty (median PFS was 18.4, 8.0, and 7.4months for pre-frail, frail, and severely frail, respectively (p=0.11). As age>65 was also predictive of shorter OS (HR=1.78, 95% CI [1.23-2.56]). We generated a novel scoring system incorporating age and frailty status. The median survival of patients that scored 0, 1, 2, and 3 points were 21.1, 18.3, 8.9, and 5.6months, respectively (p<0.001). The area under the curve of the validation cohort using a logistic regression model was 0.77. Our results indicate that the MFI-5 is a promising metric with application at the point of care to provide decision support for patients with NSCLC derived BMs.

GTPase GPN3 facilitates cell proliferation and migration in non-small cell lung cancer by impeding clathrin-mediated endocytosis of EGFR

Small GTPases play a critical role as regulatory molecules in signaling transduction and various cellular processes, contributing to the development of human diseases, including cancers. GPN3, an evolutionarily conserved member of the GPN-loop GTPase subfamily classified in 2007 according to its structure, has limited knowledge regarding its cellular functions and molecular mechanisms. In this study, we demonstrate that GPN3 interacts with clathrin light chain A (CLTA), a vesicle coat protein, as well as clathrin-mediated endocytosis associated modulators AP2B1 and AP2S1. Upregulation of GPN3 leads to the inhibition of clathrin-coated pit invagination. Furthermore, we discovered that GPN3 interacts with the epidermal growth factor receptor (EGFR) and regulates the co-localization of EGFR and CLTA, as well as the localization of EGFR in early endosomes upon EGF stimulation. As a result, this leads to a decrease in endocytic levels of EGFR and an increase in the accumulation of EGFR on the cell membrane surface, thereby prolonging activation of EGFR signaling. The functional effects exerted by GPN3 are dependent on cellular levels of GTP abundance. Furthermore, our findings indicate that aberrant overexpression of GPN3 is observed in non-small cell lung cancer (NSCLC) tissues compared to adjacent normal tissues, and high expression levels of GPN3 are associated with poor prognosis for NSCLC patients. Collectively, these findings reveal that GPN3 acts as an oncogene promoting cell proliferation and migration in NSCLC through regulation of clathrin-dependent EGFR endocytosis. These results suggest that targeting GPN3 could serve as a novel prognostic biomarker and therapeutic strategy for NSCLC treatment.

Mechanisms and strategies of immunosenescence effects on non-small cell lung cancer (NSCLC) treatment: A comprehensive analysis and future directions.

Non-small cell lung cancer (NSCLC), the most prevalent form of lung cancer, remains a leading cause of cancer-related mortality worldwide, particularly among elderly individuals. The phenomenon of immunosenescence, characterized by the progressive decline in immune cell functionality with aging, plays a pivotal role in NSCLC progression and contributes to the diminished efficacy of therapeutic interventions in older patients. Immunosenescence manifests through impaired immune surveillance, reduced cytotoxic responses, and increased chronic inflammation, collectively fostering a pro-tumorigenic microenvironment. This review provides a comprehensive analysis of the molecular, cellular, and genetic mechanisms of immunosenescence and its impact on immune surveillance and the tumor microenvironment (TME) in NSCLC. We explore how aging affects various immune cells, including T cells, B cells, NK cells, and macrophages, and how these changes compromise the immune system's ability to detect and eliminate tumor cells. Furthermore, we address the challenges posed by immunosenescence to current therapeutic strategies, particularly immunotherapy, which faces significant hurdles in elderly patients due to immune dysfunction. The review highlights emerging technologies, such as single-cell sequencing and CRISPR-Cas9, which offer new insights into immunosenescence and its potential as a therapeutic target. Finally, we outline future research directions, including strategies for rejuvenating the aging immune system and optimizing immunotherapy for older NSCLC patients, with the goal of improving treatment efficacy and survival outcomes. These efforts hold promise for the development of more effective, personalized therapies for elderly patients with NSCLC.

The association of chylothorax with aggressiveness of lymph node management during pulmonary resection.

Chylothorax is a morbid and costly complication that can originate in lymph node resection beds. We hypothesized a close association between the occurrence of chylothorax and the extent/aggressiveness of lymph node dissection. We conducted a nested case-control study of 1728 non-small cell lung cancer patients who underwent resection at our institution January 2005-July 2023. Cases were defined as patients who developed chylothorax. Each case was matched with 3 control subjects who did not develop chylothorax, based on year of diagnosis, clinical N-descriptor, presence of granulomatous lymph nodes, extent of resection, and tumor laterality. Using conditional logistic regression, we estimated risk ratios with 95% confidence intervals to examine the association between the occurrence of chylothorax and several measures of the extent of lymph node resection. The incidence of chylothorax was 33/1728 (1.9%). In the matched groups, patients with chylothorax had higher rates of complete lymphadenectomy (82% vs. 65%, p=0.059) and systematic lymph node dissection as defined by IASLC/ESMO/ESTS (85% vs. 52%, p=0.002). Station 2 was resected significantly more often in the chylothorax group (48.5% vs. 29%, p=0.04). The chylothorax group had a longer median in-hospital stay (7 vs. 4 days, p=0.003), and higher reoperation (18% vs. 1.0%, p=0.006) and readmission (18% vs. 5%, p=0.03) rates. In matched groups, chylothorax is associated with several measures of more aggressive lymph node management and results in substantial postoperative morbidity. This finding provides additional support for more selective lymph node management approaches when resecting smaller, less-solid, less 18-fluorodeoxyglucose-avid tumors.